Generative AI (GenAI) tools have revolutionised modern workflows, offering unprecedented efficiency and innovation. However, these benefits come with significant risks related to data security. As organisation's increasingly adopt AI tools, concerns about sensitive data being inadvertently shared and potentially used to train these systems have become paramount.

Enterprise users are leaking sensitive corporate data through use of unauthorised and authorised generative AI apps at alarming rates. Plugging the leaks is vital to reduce risk exposure.

Gen AI data leaks from employees are an enterprise nightmare in the making.

A study analyzing tens of thousands of prompts to ChatGPT, MS Copilot, Gemini, Claude, and Perplexity during Q4 2024 found that customer data, including billing information and authentication data, made up the largest share of leaked data at 46%. The findings highlighted that insurance claims, often containing sensitive customer details, are frequently entered into GenAI tools by employees to expedite processing.

Employee data, including payroll data and personally identifiable information (PII), accounted for 27% of sensitive prompts, followed by legal and finance data at 15%.

Security-related information, comprising 6.88% of sensitive prompts, is particularly concerning,” according to the report. “Examples include penetration test results, network configurations, and incident reports. Such data could provide attackers with a blueprint for exploiting vulnerabilities.”

GenAI: A Game-Changer with Hidden Risks

Generative AI (GenAI) refers to artificial intelligence models that can create new content, such as text, images, or music, based on patterns learned from existing data. Tools like ChatGPT, MS Copilot, Gemini, Claude, and Perplexity are examples of GenAI that have become integral to various industries. These tools promise to streamline processes, enhance creativity, and accelerate innovation. However, the very data that powers these tools can also pose significant risks if not managed properly.

The rapid adoption of GenAI by organisation's can be attributed to several key factors:

1. Efficiency and Productivity: GenAI tools can automate repetitive tasks, freeing up employees to focus on more strategic and creative work. For example, ChatGPT can draft reports, summarise meetings, and even generate code snippets, significantly reducing the time spent on these tasks.

2. Innovation and Creativity: GenAI unlocks fresh ideas, unique designs, and innovative solutions that may not have emerged through human effort alone. This is especially beneficial in fields like marketing, advertising, and product design, where creativity plays a crucial role in success.

3. Data-Driven Decisions: GenAI can analyze massive datasets to uncover insights and provide recommendations, helping organizations predict market trends, optimize supply chains, and personalize customer experiences.

4. Cost Saving: By automating tasks and improving efficiency, GenAI can lead to significant cost saving. This is particularly important in industries with tight margins, where even small improvements in efficiency can have a substantial impact on the bottom line.

5. Competitive Advantage: Organisation's that effectively leverage GenAI can gain a competitive edge by bringing products to market faster, offering more personalised customer experiences, and making better strategic decisions.

Balancing Adoption and Data Security: The GenAI Challenge

Organisation's face a critical dilemma: adopting GenAI to stay competitive or risking data exposure that could compromise their competitive edge. The research report highlights that while GenAI tools promise efficiency, they also pose significant data security risks. The concern is not unfounded, as high-profile cases like Amazon and Samsung's unintended data exposure through GenAI tools have underscored the risks

A Cisco study revealed that 48% of organisation's reported non-public company information being entered into GenAI tools by employees, and 68% worried about the risk of this information being disclosed to competitors or the public.

What Are Employees Putting into GenAI?

A study analyzing 10,000 prompts from users of popular GenAI tools found that while most prompts were harmless, 8.5% contained sensitive information. The types of data frequently entered into these systems include:

1. Customer Data (45.77%) – This category includes billing details, authentication data, payment transactions, customer profiles, and dispute resolution reports. Insurance claims are a prime example, as they often contain highly sensitive personal and financial information. While employees use GenAI to expedite processing, this practice risks exposing confidential customer data.

2. Employee Data (26.83%) – Payroll details, personally identifiable information (PII), and employment records frequently appear in prompts. HR-related tasks such as performance reviews, hiring decisions, and compensation planning often lead to unintentional data leaks.

3. Legal and Finance Data (14.88%) – This includes investment portfolios, financial projections, mergers & acquisitions (M&A) data, and patent information. Many legal teams use GenAI for spell checks, translations, or summarization, sometimes exposing confidential contracts or sensitive business negotiations. Similarly, financial analysts may input sales pipeline data or competitive insights, increasing regulatory and privacy risks.

4. Security Data (6.88%) – Employees have entered access control policies, network configurations, incident reports, and security protocols into GenAI tools. Such information could give attackers a roadmap to exploit vulnerabilities, making this category particularly concerning.

5. Sensitive Code (5.64%) – Proprietary source code, API keys, and credentials have also been found in GenAI prompts. If this information is used in model training, it can lead to intellectual property leakage and competitive disadvantages.

Categories of Data Entered into GenAI Applications

Many organizations unknowingly expose sensitive business data while using GenAI tools. From customer records and financial data to internal security details, these inputs increase the risk of data breaches, compliance violations, and intellectual property loss.

Organizational Perspectives on GenAI Security

Businesses recognize both the efficiency benefits and security risks of GenAI adoption. While these tools streamline operations, organizations remain concerned about data privacy, regulatory compliance, and security vulnerabilities.

The Risks of Free Tiers

Many organizations are affected by Shadow AI, where sensitive data is knowingly or unknowingly exposed to LLM service providers. This issue is further complicated by the availability of free-tier GenAI tools. Employees often input sensitive information into these tools, heightening the risk of data leakage. Free-tier services typically state that they train on customer data, meaning any entered information could be used to improve models, exacerbating the risks.

In 2024,

• 63.8% of ChatGPT users used the free tier, with 53.5% of sensitive prompts entered here.
• Similarly, 58.62% of Gemini users, 75% of Claude users, and 50.48% of Perplexity users also operated on free tiers.

Responsible AI Governance and Data Security

Spro from Hrida AI emerges as an essential solution for businesses seeking to harness Generative AI (GenAI) while safeguarding sensitive information. This secure, AI-driven platform is designed to uphold data privacy and compliance, enabling seamless integration of GenAI capabilities without compromising user trust or regulatory obligations.

Key Features of Spro:

• Data Privacy and Compliance: Spro emphasizes the security of sensitive information, ensuring all interactions with GenAI models comply with strict privacy standards and regulatory mandates.
• Secure Integration:The platform provides a robust and scalable solution that integrates effortlessly with existing business workflows, eliminating the necessity for costly in-house AI infrastructure.
• Dual Protection Approach:Spro offers comprehensive security measures to protect both AI model developers and end-users, safeguarding intellectual property and sensitive data from potential threats.

By adopting Spro, businesses can confidently leverage GenAI technologies while maintaining stringent data protection and compliance standards.

Conclusion

Generative AI is transforming enterprise workflows, but its rapid adoption comes with substantial data security risks. Organizations must balance innovation with responsible AI governance to prevent sensitive data from being inadvertently exposed. Without the right safeguards, companies risk regulatory non-compliance, intellectual property leaks, and competitive disadvantages.

Spro from Hrida AI provides a critical security layer, enabling businesses to harness GenAI’s potential while maintaining strict data privacy and compliance. By adopting proactive AI governance measures, organizations can mitigate risks, protect sensitive information, and ensure GenAI enhances productivity—without compromising security.